Background Many studies in radiomics are using feature selection methods to identify the
most predictive features. At the same time, they employ cross-validation to estimate the …

Objectives A critical problem in radiomic studies is the high dimensionality of the datasets,
which stems from small sample sizes and many generic features extracted from the volume …

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and
treatment can increase the cancer survival rate. In this context, thermography is a suitable …

This paper studies the effects that combinations of balancing and feature selection
techniques have on wide data (many more attributes than instances) when different …

The most common preprocessing techniques used to deal with datasets having high
dimensionality and a low number of instances—or wide data—are feature reduction (FR) …

Feature selection is an essential process in machine learning in selecting the features that
contribute the most to the prediction target to build more interpretable and robust models …

The classification of high dimensional gene expression data is key to the development of
effective diagnostic and prognostic tools. Feature selection involves finding the best subset …

Cadmium (Cd) is present in soils and can easily migrate into plants due to its various forms.
This mobility allows it to be absorbed by plant roots and accumulate in edible parts, entering …

The Quantitative Structure-Activity Relationship (QSAR) approach for predicting the
biological activity and physicochemical properties of mixtures is gaining prominence, driven …

To quickly achieve nondestructive detection of protein content in fresh milk, this study
utilized a network analyzer and an open coaxial probe to analyze the dielectric spectra of …